Determining protein complex connectivity using a probabilistic deletion network derived from quantitative proteomics

PLoS One. 2009 Oct 6;4(10):e7310. doi: 10.1371/journal.pone.0007310.

Abstract

Protein complexes are key molecular machines executing a variety of essential cellular processes. Despite the availability of genome-wide protein-protein interaction studies, determining the connectivity between proteins within a complex remains a major challenge. Here we demonstrate a method that is able to predict the relationship of proteins within a stable protein complex. We employed a combination of computational approaches and a systematic collection of quantitative proteomics data from wild-type and deletion strain purifications to build a quantitative deletion-interaction network map and subsequently convert the resulting data into an interdependency-interaction model of a complex. We applied this approach to a data set generated from components of the Saccharomyces cerevisiae Rpd3 histone deacetylase complexes, which consists of two distinct small and large complexes that are held together by a module consisting of Rpd3, Sin3 and Ume1. The resulting representation reveals new protein-protein interactions and new submodule relationships, providing novel information for mapping the functional organization of a complex.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Cluster Analysis
  • Gene Deletion
  • Genome
  • Histone Deacetylases / metabolism
  • Models, Biological
  • Models, Statistical
  • Probability
  • Protein Interaction Mapping / methods*
  • Proteins / chemistry*
  • Proteomics / methods*
  • Repressor Proteins / metabolism
  • Saccharomyces cerevisiae / metabolism
  • Saccharomyces cerevisiae Proteins / metabolism

Substances

  • Proteins
  • Repressor Proteins
  • SIN3 protein, S cerevisiae
  • Saccharomyces cerevisiae Proteins
  • UME1 protein, S cerevisiae
  • RPD3 protein, S cerevisiae
  • Histone Deacetylases